import librosa
import librosa.filters
import numpy as np
from scipy import signal
from scipy.io import wavfile
from .hparams import HParams as hp


def load_wav(path, sr):
    return librosa.core.load(path, sr=sr)[0]


def save_wav(wav, path, sr):
    wav *= 32767 / max(0.01, np.max(np.abs(wav)))
    # proposed by @dsmiller
    wavfile.write(path, sr, wav.astype(np.int16))


def save_wavenet_wav(wav, path, sr):
    librosa.output.write_wav(path, wav, sr=sr)


def preemphasis(wav, k, preemphasize=True):
    if preemphasize:
        return signal.lfilter([1, -k], [1], wav)
    return wav


def inv_preemphasis(wav, k, inv_preemphasize=True):
    if inv_preemphasize:
        return signal.lfilter([1], [1, -k], wav)
    return wav


def get_hop_size():
    hop_size = hp['hop_size']
    if hop_size is None:
        assert hp['frame_shift_ms'] is not None
        hop_size = int(hp['frame_shift_ms'] / 1000 * hp['sample_rate'])
    return hop_size


def linearspectrogram(wav):
    D = _stft(preemphasis(wav, hp['preemphasis'], hp['preemphasize']))
    S = _amp_to_db(np.abs(D)) - hp['ref_level_db']

    if hp['signal_normalization']:
        return _normalize(S)
    return S


def melspectrogram(wav):
    D = _stft(preemphasis(wav, hp['preemphasis'], hp['preemphasize']))
    S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp['ref_level_db']

    if hp['signal_normalization']:
        return _normalize(S)
    return S


def _lws_processor():
    import lws
    return lws.lws(hp['n_fft'], get_hop_size(), fftsize=hp['win_size'], mode='speech')


def _stft(y):
    if hp['use_lws']:
        return _lws_processor(hp).stft(y).T
    else:
        return librosa.stft(y=y, n_fft=hp['n_fft'], hop_length=get_hop_size(), win_length=hp['win_size'])

##########################################################
# Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)


def num_frames(length, fsize, fshift):
    '''
    Compute number of time frames of spectrogram
    '''
    pad = (fsize - fshift)
    if length % fshift == 0:
        M = (length + pad * 2 - fsize) // fshift + 1
    else:
        M = (length + pad * 2 - fsize) // fshift + 2
    return M


def pad_lr(x, fsize, fshift):
    '''
    Compute left and right padding
    '''
    M = num_frames(len(x), fsize, fshift)
    pad = (fsize - fshift)
    T = len(x) + 2 * pad
    r = (M - 1) * fshift + fsize - T
    return pad, pad + r
##########################################################
# Librosa correct padding


def librosa_pad_lr(x, fsize, fshift):
    return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]


# Conversions
_mel_basis = None


def _linear_to_mel(spectogram):
    global _mel_basis
    if _mel_basis is None:
        _mel_basis = _build_mel_basis()
    return np.dot(_mel_basis, spectogram)


def _build_mel_basis():
    assert hp['fmax'] <= hp['sample_rate'] // 2
    return librosa.filters.mel(sr=hp['sample_rate'], n_fft=hp['n_fft'], n_mels=hp['num_mels'],
                               fmin=hp['fmin'], fmax=hp['fmax'])


def _amp_to_db(x):
    min_level = np.exp(hp['min_level_db'] / 20 * np.log(10))
    return 20 * np.log10(np.maximum(min_level, x))


def _db_to_amp(x):
    return np.power(10.0, (x) * 0.05)


def _normalize(S):
    if hp['allow_clipping_in_normalization']:
        if hp['symmetric_mels']:
            return np.clip((2 * hp['max_abs_value']) * ((S - hp['min_level_db']) / (-hp['min_level_db'])) - hp['max_abs_value'],
                           -hp['max_abs_value'], hp['max_abs_value'])
        else:
            return np.clip(hp['max_abs_value'] * ((S - hp['min_level_db']) / (-hp['min_level_db'])), 0, hp['max_abs_value'])

    assert S.max() <= 0 and S.min() - hp['min_level_db'] >= 0
    if hp['symmetric_mels']:
        return (2 * hp['max_abs_value']) * ((S - hp['min_level_db']) / (-hp['min_level_db'])) - hp['max_abs_value']
    else:
        return hp['max_abs_value'] * ((S - hp['min_level_db']) / (-hp['min_level_db']))


def _denormalize(D):
    if hp['allow_clipping_in_normalization']:
        if hp['symmetric_mels']:
            return (((np.clip(D, -hp['max_abs_value'],
                              hp['max_abs_value']) + hp['max_abs_value']) * -hp['min_level_db'] / (2 * hp['max_abs_value']))
                    + hp['min_level_db'])
        else:
            return ((np.clip(D, 0, hp['max_abs_value']) * -hp['min_level_db'] / hp['max_abs_value']) + hp['min_level_db'])

    if hp['symmetric_mels']:
        return (((D + hp['max_abs_value']) * -hp['min_level_db'] / (2 * hp['max_abs_value'])) + hp['min_level_db'])
    else:
        return ((D * -hp['min_level_db'] / hp['max_abs_value']) + hp['min_level_db'])
